CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.

Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.

rcCAE: a convolutional autoencoder method for detecting intra-tumor heterogeneity and single-cell copy number alterations.

Intra-tumor heterogeneity (ITH) is one of the major confounding factors that result in cancer relapse, and deciphering ITH is essential for personalized therapy. Single-cell DNA sequencing (scDNA-seq) now enables profiling of single-cell copy number alterations (CNAs) and thus aids in high-resolution inference of ITH. Here, we introduce an integrated framework called rcCAE to accurately infer cell subpopulations and single-cell CNAs from scDNA-seq data. A convolutional autoencoder (CAE) is employed in rcCAE to learn latent representation of the cells as well as distill copy number information from noisy read counts data. This unsupervised representation learning via the CAE model makes it convenient to accurately cluster cells over the low-dimensional latent space, and detect single-cell CNAs from enhanced read counts data. Extensive performance evaluations on simulated datasets show that rcCAE outperforms the existing CNA calling methods, and is highly effective in inferring clonal architecture. Furthermore, evaluations of rcCAE on two real datasets demonstrate that it is able to provide a more refined clonal structure, of which some details are lost in clonal inference based on integer copy numbers.

Inferring single-cell copy number profiles through cross-cell segmentation of read counts.

BACKGROUND: Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data. RESULTS: By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation-maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna . CONCLUSIONS: Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data.

BACKGROUND: Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. RESULTS: We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . CONCLUSIONS: Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Ternary-Metal Sn-Pb-Zn Perovskite to Reconstruct Top Surface for Efficient and Stable Less-Pb Perovskite Solar Cells.

Though Sn-Pb alloyed perovskite solar cells (PSCs) achieved great progress, there is a dilemma to further increase Sn for less-Pb requirement. High Sn ratio (>70%) perovskite exhibits nonstoichiometric Sn:Pb:I at film surface to aggravate Sn2+ oxidation and interface energy mismatch. Here, ternary metal alloyed (FASnI3 )0.7 (MAPb1- x Znx I3 )0.3 (x = 0-3%) is constructed for Pb% < 30% perovskite. Zn with smaller ionic size and stronger ionic interaction than Sn/Pb assists forming high-quality perovskite film with ZnI6 4- enriched at surface to balance Sn:Pb:I ratio. Differing from uniform bulk doping, surface-rich Zn with lower lying orbits pushes down the energy band of perovskite and adjusts the interface energy for efficient charge transfer. The alloyed PSC realizes efficiency of 19.4% at AM1.5 (one of the highest values reported for Pb% < 30% PSCs). Moreover, stronger bonding of Zn─I and Sn─I contributes to better durability of ternary perovskite than binary perovskite. This work highlights a novel alloy method for efficient and stable less-Pb PSCs.

bmVAE: a variational autoencoder method for clustering single-cell mutation data.

MOTIVATION: Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data. RESULTS: We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. AVAILABILITY AND IMPLEMENTATION: bmVAE is freely available at https://github.com/zhyu-lab/bmvae. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AMC: accurate mutation clustering from single-cell DNA sequencing data.

SUMMARY: Single-cell DNA sequencing (scDNA-seq) now enables high-resolution profiles of intra-tumor heterogeneity. Existing methods for phylogenetic inference from scDNA-seq data perform acceptably well on small datasets but suffer from low computational efficiency and/or degraded accuracy on large datasets. Motivated by the fact that mutations sharing common states over single cells can be grouped together, we introduce a new software called AMC (accurate mutation clustering) to accurately cluster mutations, thus improve the efficiency of phylogenetic inference. AMC first employs principal component analysis followed by K-means clustering to find mutation clusters, then infers the maximum likelihood estimates of the genotypes of each cluster. The inferred genotypes can subsequently be used to reconstruct the phylogenetic tree with high efficiency. Comprehensive evaluations on various simulated datasets demonstrate AMC is particularly useful to efficiently reason the mutation clusters on large scDNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: AMC is freely available at https://github.com/qasimyu/amc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dynamical Transition of Operator Size Growth in Quantum Systems Embedded in an Environment.

In closed generic many-body systems, unitary evolution disperses local quantum information into highly nonlocal objects, resulting in thermalization. Such a process is called information scrambling, whose swiftness is quantified by the operator size growth. However, the impact of couplings to the environment on the process of information scrambling remains unexplored for quantum systems embedded within an environment. Here we predict a dynamical transition in quantum systems with all-to-all interactions accompanied by an environment, which separates two phases. In the dissipative phase, information scrambling halts as the operator size decays with time, while in the scrambling phase, dispersion of information persists, and the operator size grows and saturates to an O(N) value in the long-time limit with N the number of degrees of freedom of the systems. The transition is driven by the competition between the system's intrinsic and environment propelled scramblings and the environment-induced dissipation. Our prediction is derived from a general argument based on epidemiological models and demonstrated analytically via solvable Brownian Sachdev-Ye-Kitaev models. We provide further evidence which suggests that the transition is generic to quantum chaotic systems when coupled to an environment. Our study sheds light on the fundamental behavior of quantum systems in the presence of an environment.

Ultrasensitive FET biosensor chip based on self-assembled organic nanoporous membrane for femtomolar detection of Amyloid-ß.

Early diagnosis of Alzheimer's disease (AD) is critical for preventing disease progression, however, the diagnosis of AD remains challenging for most patients due to limitations of current sensing technologies. A common pathological feature found in AD-affected brains is the accumulation of Amyloid-ß (Aß) polypeptides, which lead to neurofibrillary tangles and neuroinflammatory plaques. Here, we developed a portable ultrasensitive FET biosensor chip based on a self-assembled nanoporous membrane for ultrasensitive detection of Aß protein in complex environments. The microscale semiconductor channel was covered with a self-assembled organic nanoporous membrane modified by antibody molecules to pick up and amplify the Aß protein signal. The nanoporous structure helps protect the sensitive channel from non-target proteins and improves its stability since no chemical functionalization process involved, largely reduces background noise of the sensing platform. When a bio-gated target is captured, the doping state of the polymer bulk could be tuned and amplified the strength of the weak signal, achieving ultrasensitive detecting performance (enabling the device to detect target protein less than 1 fg/ml in 1 µl sample). Moreover, the device simplifies the circuit connection by integrating all the connections on a 2 cm × 2 cm chip, avoiding expensive and complex manufacturing processes, and makes it usable for portable prognosis. We believe that this ultrasensitive, portable, low-cost Aß sensor chip shows the great potential in the early diagnosis of AD and large-scale population screening applications.

Paenibacillus polygoni sp. nov., an endophytic bacterium isolated from Polygonum lapathifolium L. in wetland.

A Gram-stain-positive, aerobic, rod-shaped, non-motile, yellowish and glossy strain, C31T, was isolated from a wetland plant Polygonum lapathifolium L. located south of Poyang Lake, Jiangxi Province, PR China. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain C31T showed similarity values of lower than 97.0 % to other type species belonging to the genus Paenibacillus. The genomic average nucleotide identity values between strain C31T and its reference type species ranged from 68.9-70.9 % and the digital DNA-DNA hybridization values were lower than 27.8 %. The genomic DNA G+C content of strain C31T was 41.9 mol%. The optimal growth temperature, pH and NaCl concentration were 37 °C, pH 7 and 0.5 %, respectively. The major cellular fatty acids (>5.0 %) of strain C31T were anteiso-C15 : 0 (73.7 %), anteiso-C17 : 0 (8.4 %) and iso-C15 : 0 (5.2 %). The polar lipids of strain C31T were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine and unidentified phospholipids. The respiratory quinone was MK-7. Based on these phylogenetic and phenotypic characterizations, strain C31T represents a novel species within the genus Paenibacillus. Therefore, the proposed name for this newly identified species is Paenibacillus polygoni sp. nov. and the type strain is C31T (=CCTCC AB 2022349T=KCTC 43565T).

Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics.

The classical three-stage model of stochastic gene expression predicts the statistics of single cell mRNA and protein number fluctuations as a function of the rates of promoter switching, transcription, translation, degradation and dilution. While this model is easily simulated, its analytical solution remains an unsolved problem. Here we modify this model to explicitly include cell-cycle dynamics and then derive an exact solution for the time-dependent joint distribution of mRNA and protein numbers. We show large differences between this model and the classical model which captures cell-cycle effects implicitly via effective first-order dilution reactions. In particular we find that the Fano factor of protein numbers calculated from a population snapshot measurement are underestimated by the classical model whereas the correlation between mRNA and protein can be either over- or underestimated, depending on the timescales of mRNA degradation and promoter switching relative to the mean cell-cycle duration time.

PDMS-PUA bi-directional replication technology and its applications.

Polydimethylsiloxane (PDMS) and polyurethane acrylate (PUA) are excellent pattern transfer materials. In this study, PDMS-PUA bi-directional replication technology is explored using the PDMS grating as a template, and relevant technical issues are discussed in detail. Special surface treatment and process optimization are applied to solve the problems of demolding, PDMS polymerization inhibition, and substrate flatness. Further experiments show that the technology can be employed to replicate nanoscale structures and has the potential value of prolonging the longevity of the original template. Additionally, utilizing the advantage of the high elasticity of PDMS materials, two applications of bi-directional replication technology are demonstrated. One is to increase the line-density of the grating by stretching, and the experimental results show that the line-density of the grating increased by 26.6%. The other one is to fabricate the convex grating. Compared with the original planar PDMS grating, the resolution of the first-order diffraction spectrum of the convex grating at the focal point has been greatly improved. Since this technology requires simple equipment, and PDMS and PUA are reusable, it has the advantages of low cost, simplicity, and rapid fabrication. The two application examples also indicate that the technology has good application value.

Null Broadening Robust Adaptive Beamforming Algorithm Based on Power Estimation.

In order to solve the problem of severely decreased performance under the situation of rapid moving sources and unstable array platforms, a null broadening robust adaptive beamforming algorithm based on power estimation is proposed in this paper. First of all, we estimate the interference signal power according to the characteristic subspace theory. Then, the correspondence between the signal power and steering vector (SV) is obtained based on the orthogonal property, and the interference covariance matrix (ICM) is reconstructed. Finally, with the aim of setting virtual interference sources, null broadening can be carried out. The proposed algorithm results in a deeper null, lower side lobes and higher tolerance of the desired signal steering vector mismatch under the condition of low complexity. The simulation results show that the algorithm also has stronger robustness.

The impact of vaccination on the spread of COVID-19: Studying by a mathematical model.

The global spread of COVID-19 has not been effectively controlled, posing a huge threat to public health and the development of the global economy. Currently, a number of vaccines have been approved for use and vaccination campaigns have already started in several countries. This paper designs a mathematical model considering the impact of vaccination to study the spread dynamics of COVID-19. Some basic properties of the model are analyzed. The basic reproductive number ℜ 1 of the model is obtained, and the conditions for the existence of endemic equilibria are provided. There exist two endemic equilibria when ℜ 1 < 1 under certain conditions, which will lead to backward bifurcation. The stability of equilibria are analyzed, and the condition for the backward bifurcation is given. Due to the existence of backward bifurcation, even if ℜ 1 < 1 , COVID-19 may remain prevalent. Sensitivity analysis and simulations show that improving vaccine efficacy can control the spread of COVID-19 faster, while increasing the vaccination rate can reduce and postpone the peak of infection to a greater extent. However, in reality, the improvement of vaccine efficacy cannot be realized in a short time, and relying only on increasing the vaccination rate cannot quickly achieve the control of COVID-19. Therefore, relying only on vaccination may not completely and quickly control COVID-19. Some non-pharmaceutical interventions should continue to be enforced to combat the virus. According to the sensitivity analysis, we improve the model by including some non-pharmaceutical interventions. Combining the sensitivity analysis with the simulation of the improved model, we conclude that together with vaccination, reducing the contact rate of people and increasing the isolation rate of infected individuals will greatly reduce the number of infections and shorten the time of COVID-19 spread. The analysis and simulations in this paper can provide some useful suggestions for the prevention and control of COVID-19.

A spread model of COVID-19 with some strict anti-epidemic measures.

In the absence of specific drugs and vaccines, the best way to control the spread of COVID-19 is to adopt and diligently implement effective and strict anti-epidemic measures. In this paper, a mathematical spread model is proposed based on strict epidemic prevention measures and the known spreading characteristics of COVID-19. The equilibria (disease-free equilibrium and endemic equilibrium) and the basic regenerative number of the model are analyzed. In particular, we prove the asymptotic stability of the equilibria, including locally and globally asymptotic stability. In order to validate the effectiveness of this model, it is used to simulate the spread of COVID-19 in Hubei Province of China for a period of time. The model parameters are estimated by the real data related to COVID-19 in Hubei. To further verify the model effectiveness, it is employed to simulate the spread of COVID-19 in Hunan Province of China. The mean relative error serves to measure the effect of fitting and simulations. Simulation results show that the model can accurately describe the spread dynamics of COVID-19. Sensitivity analysis of the parameters is also done to provide the basis for formulating prevention and control measures. According to the sensitivity analysis and corresponding simulations, it is found that the most effective non-pharmaceutical intervention measures for controlling COVID-19 are to reduce the contact rate of the population and increase the quarantine rate of infected individuals.

SCSsim: an integrated tool for simulating single-cell genome sequencing data.

MOTIVATION: Allele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency. AVAILABILITY AND IMPLEMENTATION: SCSsim is freely available at https://github.com/qasimyu/scssim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rosai-Dorfman disease in the central nervous system with two isolated lesions originated from a single clone: a case report.

BACKGROUND: Rosai-Dorfman disease (RDD) is a rare, benign, idiopathic non-Langerhans cell histiocytosis. Cases of RDD in the CNS are extremely rare but lethal. RDD is thought to represent a reactive process. Recent studies proposed a subset of RDD cases that had a clonal nature. However, its clone origin is poorly understood. CASE PRESENTATION: We present a rare case of RDD in the CNS with two isolated lesions. These two lesions were removed successively after two operations. No seizure nor recurrence appears to date (2 years follow-up). Morphological and immunohistochemical profiles of these two lesions support the diagnosis of RDD. Based on the whole-exome sequencing (WES) data, we found the larger lesion has a higher tumor mutational burden (TMB) and more driver gene mutations than the smaller lesion. We also found seven common truncal mutations in these two lesions, raising the possibility that they might stem from the same ancestor clone. CONCLUSIONS: Overall, this is the first report about clonal evolution of RDD in the CNS with two isolated lesions. Our findings contribute to the pathology of RDD, and support the notion that a subset of cases with RDD is a clonal histiocytic disorder driven by genetic alterations.

Potential role of organic matter in the transmission of antibiotic resistance genes in black soils.

The degradation of black soil is a serious problem with the decrease in soil organic matter (SOM) content in northeast China, and animal manure as a reservoir of antibiotic resistance genes (ARGs) is commonly amended into soil to sustain or increase the SOM content. However, the potential effect of SOM content on soil resistome remains unclear. Here, a soil microcosm experiment was established to explore the temporal succession of antibiotic resistance genes (ARGs) and bacterial communities in three black soils with distinct difference in SOM contents following application of poultry manure using high-throughput qPCR (HT-qPCR) and MiSeq sequencing. A total of 151 ARGs and 8 mobile genetic elements (MGEs) were detected across all samples. Relative abundance of ARGs negatively correlated with SOM content. Manure-derived ARGs had much higher diversity and absolute abundance in the low SOM soils. The ARG composition and bacterial community structure were significantly different in three soils. A random forest model showed that SOM content was a better predictor of ARG pattern than bacterial diversity and abundance. Structural equation modeling indicated that the negative effects of SOM content on ARG patterns was accomplished by the shift of bacterial communities such as the bacterial diversity and abundance. Our study demonstrated that SOM content could play an important role in the dissemination of ARGs originated from animal manures, these findings provide a possible strategy for the suppression of the spread of ARGs in black soils by increasing SOM content.

Self organizing maps for the parametric analysis of COVID-19 SEIRS delayed model.

Since 2019, entire world is facing the accelerating threat of Corona Virus, with its third wave on its way, although accompanied with several vaccination strategies made by world health organization. The control on the transmission of the virus is highly desired, even though several key measures have already been made, including masks, sanitizing and disinfecting measures. The ongoing research, though devoted to this pandemic, has certain flaws, due to which no permanent solution has been discovered. Currently different data based studies have emerged but unfortunately, the pandemic fate is still unrevealed. During this research, we have focused on a compartmental model, where delay is taken into account from one compartment to another. The model depicts the dynamics of the disease relative to time and constant delays in time. A deep learning technique called "Self Organizing Map" is used to extract the parametric values from the data repository of COVID-19. The input we used for SOM are the attributes on which, the variables are dependent. Different grouping/clustering of patients were achieved with 2- dimensional visualization of the input data ( h t t p s : / / c r e a t i v e c o m m o n s . o r g / l i c e n s e s / b y / 2.0 / ). Extensive stability analysis and numerical results are presented in this manuscript which can help in designing control measures.

Forecasting the impact of environmental stresses on the frequent waves of COVID19.

A novel approach to link the environmental stresses with the COVID-19 cases is adopted during this research. The time-dependent data are extracted from the online repositories that are freely available for knowledge and research. Since the time series data analysis is desired for the COVID-19 time-dependent frequent waves, here in this manuscript, we have developed a time series model with the aid of "nonlinear autoregressive network with exogenous inputs (NARX)" approach. The distribution of infectious agent-containing droplets from an infected person to an uninfected person is a common form of respiratory disease transmission. SARS-CoV-2 has mainly spread via short-range respiratory droplet transmission. Airborne transmission of SARS-CoV-2 seems to have occurred over long distances or times in unusual conditions; SARS-CoV-2 RNA was found in PM10 collected in Italy. This research shows that SARS-CoV-2 particles adsorbed to outdoor PM remained viable for a long time, given the epidemiology of COVID-19, outdoor air pollution is unlikely to be a significant route of transmission. In this research, ANN time series is used to analyze the data resulting from the COVID-19 first and second waves and the forecasted results show that air pollution affects people in different areas of Italy and make more people sick with covid-19. The model is developed based on the disease transmission data of Italy.